Once you learn how to spot a Flock Safety camera, you start seeing them everywhere. There are over 80,000 such AI-powered automated license plate recognition (ALPR) devices across the US today, collectively scanning tens of billions of plates every month. Beyond your license plate numbers, Flock Safety cameras also capture your vehicle’s make and model, color, and any visible distinguishing features, such as trailers, toolboxes, bumper stickers, or bike racks.
Police departments, neighborhood HOAs, businesses, and private property owners can all subscribe annually to lease Flock cameras and software. And while Flock insists the system is merely a vehicle-identification tool, that doesn’t make it any easier to stomach their existence. It’s hard not to feel unsettled by them… and that’s before you get into any one of the many controversies surrounding these devices.
These aren’t like CCTV cameras. They aren’t the same as traffic light cameras, either. According to Flock, the cameras can only document what’s already visible from public roads. They don’t collect any facial recognition data or other biometric info from you. Instead, Flock says its cameras are simply meant to help generate investigative leads, without using any personal information beyond a person’s “vehicle fingerprint.” The company also says any collected data is automatically deleted after 30 days (unless local laws require a different retention period). Still, no matter what Flock says in its PR-speak, there’s no denying the ways this data can be (and has already been) abused.
Flock Safety data is being misused, and Flock’s not doing enough to stop it
bluestork/Shutterstock
The biggest problem lies in Flock’s nationwide database. This massive trove of “vehicle fingerprints” allows law enforcement agencies to search vehicle records collected nationwide. Thanks to Flock, those with access can effectively create a detailed record of where people travel and when. And by expanding access beyond law enforcement to private-sector organizations, Flock cameras create new opportunities for watch lists, blacklists, and broader forms of monitoring outside the law.
Traffic violations are one thing. But in recent years, this Flock camera information has also been used to assist in immigration enforcement, track abortion-related cases, and even stalk innocent civilians. Nevertheless, Flock takes something of a hands-off approach to its data (and its misuse). Per Flock’s site, “the customer owns the data, decides whether to share it, and can manage access based on its own policies and needs.” All things considered, it feels like they’re playing dumb about what’s really going on.
At this point, it hardly matters whether the cameras collect facial recognition data. Vehicle information can clearly be just as invasive. Through it all, Flock continually tries to argue its data isn’t what it obviously is: de facto mass surveillance. Flock is both denying culpability and doing next to nothing to stop the data from being misused. That should concern every American, not just those behind the wheel.
Staff who use AI can end up with more to do, not less.
Think carefully about the tools you’re using and why.
Adopt a set of standards and refine your outputs.
The promise of productivity boosts from AI can come with an unwelcome side order of stress. Harvard Business Review found that AI doesn’t reduce work; it intensifies it, leading to cognitive fatigue and unsustainable hours.
While the common perception is that AI can help reduce workloads, allowing employees to focus more on higher-value and more engaging tasks, HBR’s research found that staff using AI worked more quickly and often ended up with more to do, not less.
Ankur Anand, group CIO at tech recruiter Harvey Nash, said professionals who want to avoid cognitive fatigue must understand how to use AI effectively and its potential risks.
“That focus will help to reduce the noise around the workload that AI creates,” he told ZDNET, suggesting that many people have unrealistic expectations about the productivity boost that AI will provide.
“Many organizations are telling their people, ‘We want to understand how you’re making an impact with AI,'” he said. “But these professionals are not empowered, which means that using AI adds a lot of pressure, because they need to prove themselves on their own terms.”
If you’re going to make the most of AI at work, then you’re going to have to find an effective balance between completing tasks quickly and producing high-quality work.
Here’s how the experts believe professionals can ensure they reap the benefits, not the problems, of AI — and they suggest that you’ll need to focus on three core areas: tools, guidelines, and outputs.
Limit your toolset
Alex Read, senior enterprise product manager for data at energy provider EDF UK, told ZDNET that the best way for professionals to reap the benefits, not the challenges, of AI is to be uber-focused on tools that help you produce value in your roles.
While there are thousands of potential AI-enabled services on the market, Read said sensible professionals limit their horizons.
In his own role, for example, Read focuses on how AI can help him build a data platform and update information accurately, efficiently, and productively: “Anything outside of that scope is noise for me.”
That sentiment resonated with Nick Pearson, CIO at technology specialist Ricoh Europe, who told ZDNET it’s important to take a step back and think carefully about how an AI tool can help you produce value in your role.
“If you think about the phrase ‘gen AI,’ the tech is very good, by definition, at generating outputs,” he said. “I could go to bed in the evening, set the model to work, and we could have four new IT strategies produced overnight.”
However, quantity doesn’t necessarily mean quality. Pearson suggested it’s important to focus on AI’s blind spots, particularly as most models are trained on preexisting content.
“AI can’t inspire people, per se; it can’t naturally create something new, because it’s actually quite recursive,” he said.
“And the judgment you have to put in sometimes, on top of everything else, whether it be an ethical or a capability judgment, is not there automatically in the technology.”
It’s in this gap, said Pearson, that human experts play a critical role: “We’re toying with that concern as an organization and saying, ‘Where does AI really play an important role, versus where are we upskilling people in areas that AI probably won’t play for a long time?'”
To correct this issue, HBR said companies need to adopt an “AI practice,” or a set of norms and standards around AI use that help professionals ensure they use AI in a constrained but productive manner.
At EDF UK, Read is part of an internal AI Center of Excellence in enterprise IT, which enables policy for the effective use of AI across the wider organization.
In addition to Read, who contributes input from a data-use perspective, the group includes other tech representatives, such as the firm’s senior manager of AI, principal software engineer, and principal solution architect.
“The remit of this center is to make sure that, when the federated business units are looking to build, develop, and deploy AI services, they have platforms, guidance, best practices, architectural assets, and materials to guide them on how to safely and efficiently adopt AI and operationalize it at scale,” he said.
Some of the key themes the center considers when assessing AI tools are scalability and reusability, ensuring a proposed service doesn’t replicate one already in use.
“All new tools and services related to AI will go through that hopper and funnel to understand scope and ensure the security, regulatory, and ethical side of things are understood,” he said, suggesting that all professionals should use their organization’s pre-existing guidelines to foster an appropriate exploitation of emerging tech.
“The benefit that guided approach brings is that it allows us to be clear in our messaging around what AI services can be used, how they’re used from a use-case perspective, and ultimately, what personas are allowed to use them.”
Louise Newbury-Smith, head of UK&I at technology specialist Zoom, told ZDNET that one way to ensure your outputs are constrained is to focus on prompting.
“Use simple amendments to be specific, such as ‘Give me the top three things with the biggest impact.’ That approach should guide your prompt, rather than saying, ‘Give me everything you know about this topic.'”
Newbury-Smith said the successful use of AI is all about being smart about how it’s exploited, and that effectiveness comes down to enablement and engagement. If a prompt yields too much information, refine it until you get what you need. She said this should still be faster than trying to get answers without AI.
The basic message for professionals is that effective applications of AI are all about you staying in the loop, said Bernhard Seiser, vice president of digital, data, and IT at AOP Health.
Think before you use AI, and think again before you push your outputs around the organization.
“It doesn’t help the business if you get AI-generated emails that are many pages long, and then you need ChatGPT to summarize the text,” he told ZDNET.
Seiser said that while there are certain tasks generative AI is good at and worth using for, in the end, “you need to use your brain.”
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